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[Docs] Update Config Doc to Add WandB Hook (#8663)

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* logger hooks samples updated

* [Docs] MMDetWandB LoggerHook Details Added

* [Docs] lint test passed
pull/8799/head
AmirMasoud Nourollah 3 years ago committed by ZwwWayne
parent aa39be73c3
commit 00efd24440
2 changed files with 161 additions and 150 deletions
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  1. 156
      docs/en/tutorials/config.md
  2. 155
      docs/zh_cn/tutorials/config.md

156
docs/en/tutorials/config.md
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@ -71,8 +71,8 @@ The `train_cfg` and `test_cfg` are deprecated in config file, please specify the
```python
# deprecated
model = dict(
type=...,
...
type=...,
...
)
train_cfg=dict(...)
test_cfg=dict(...)
@ -83,10 +83,10 @@ The migration example is as below.
```python
# recommended
model = dict(
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
)
```
@ -109,8 +109,8 @@ model = dict(
type='BN', # Type of norm layer, usually it is BN or GN
requires_grad=True), # Whether to train the gamma and beta in BN
norm_eval=True, # Whether to freeze the statistics in BN
style='pytorch'， # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # The ImageNet pretrained backbone to be loaded
style='pytorch', # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # The ImageNet pretrained backbone to be loaded
neck=dict(
type='FPN', # The neck of detector is FPN. We also support 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details.
in_channels=[256, 512, 1024, 2048], # The input channels, this is consistent with the output channels of backbone
@ -182,70 +182,70 @@ model = dict(
type='CrossEntropyLoss', # Type of loss used for segmentation
use_mask=True, # Whether to only train the mask in the correct class.
loss_weight=1.0)))) # Loss weight of mask branch.
train_cfg = dict( # Config of training hyperparameters for rpn and rcnn
rpn=dict( # Training config of rpn
assigner=dict( # Config of assigner
type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples
neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples
min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples
match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details).
ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
sampler=dict( # Config of positive/negative sampler
type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=256, # Number of samples
pos_fraction=0.5, # The ratio of positive samples in the total samples.
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=False), # Whether add GT as proposals after sampling.
allowed_border=-1, # The border allowed after padding for valid anchors.
pos_weight=-1, # The weight of positive samples during training.
debug=False), # Whether to set the debug mode
rpn_proposal=dict( # The config to generate proposals during training
nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
nms_pre=2000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
max_per_img=1000, # The number of boxes to be kept after NMS.
nms=dict( # Config of NMS
type='nms', # Type of NMS
iou_threshold=0.7 # NMS threshold
),
min_bbox_size=0), # The allowed minimal box size
rcnn=dict( # The config for the roi heads.
assigner=dict( # Config of assigner for second stage, this is different for that in rpn
type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples
neg_iou_thr=0.5, # IoU < threshold 0.5 will be taken as negative samples
min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples
match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details).
ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
sampler=dict(
type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=512, # Number of samples
pos_fraction=0.25, # The ratio of positive samples in the total samples.
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=True
), # Whether add GT as proposals after sampling.
mask_size=28, # Size of mask
pos_weight=-1, # The weight of positive samples during training.
debug=False)) # Whether to set the debug mode
test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn
rpn=dict( # The config to generate proposals during testing
nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
nms_pre=1000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
max_per_img=1000, # The number of boxes to be kept after NMS.
nms=dict( # Config of NMS
type='nms', #Type of NMS
iou_threshold=0.7 # NMS threshold
),
min_bbox_size=0), # The allowed minimal box size
rcnn=dict( # The config for the roi heads.
score_thr=0.05, # Threshold to filter out boxes
nms=dict( # Config of NMS in the second stage
type='nms', # Type of NMS
iou_thr=0.5), # NMS threshold
max_per_img=100, # Max number of detections of each image
mask_thr_binary=0.5)) # Threshold of mask prediction
train_cfg = dict( # Config of training hyperparameters for rpn and rcnn
rpn=dict( # Training config of rpn
assigner=dict( # Config of assigner
type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples
neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples
min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples
match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details).
ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
sampler=dict( # Config of positive/negative sampler
type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=256, # Number of samples
pos_fraction=0.5, # The ratio of positive samples in the total samples.
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=False), # Whether add GT as proposals after sampling.
allowed_border=-1, # The border allowed after padding for valid anchors.
pos_weight=-1, # The weight of positive samples during training.
debug=False), # Whether to set the debug mode
rpn_proposal=dict( # The config to generate proposals during training
nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
nms_pre=2000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
max_per_img=1000, # The number of boxes to be kept after NMS.
nms=dict( # Config of NMS
type='nms', # Type of NMS
iou_threshold=0.7 # NMS threshold
),
min_bbox_size=0), # The allowed minimal box size
rcnn=dict( # The config for the roi heads.
assigner=dict( # Config of assigner for second stage, this is different for that in rpn
type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples
neg_iou_thr=0.5, # IoU < threshold 0.5 will be taken as negative samples
min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples
match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details).
ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
sampler=dict(
type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=512, # Number of samples
pos_fraction=0.25, # The ratio of positive samples in the total samples.
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=True
), # Whether add GT as proposals after sampling.
mask_size=28, # Size of mask
pos_weight=-1, # The weight of positive samples during training.
debug=False)) # Whether to set the debug mode
test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn
rpn=dict( # The config to generate proposals during testing
nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
nms_pre=1000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
max_per_img=1000, # The number of boxes to be kept after NMS.
nms=dict( # Config of NMS
type='nms', #Type of NMS
iou_threshold=0.7 # NMS threshold
),
min_bbox_size=0), # The allowed minimal box size
rcnn=dict( # The config for the roi heads.
score_thr=0.05, # Threshold to filter out boxes
nms=dict( # Config of NMS in the second stage
type='nms', # Type of NMS
iou_thr=0.5), # NMS threshold
max_per_img=100, # Max number of detections of each image
mask_thr_binary=0.5)) # Threshold of mask prediction
dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset
data_root = 'data/coco/' # Root path of data
img_norm_cfg = dict( # Image normalization config to normalize the input images
@ -381,7 +381,7 @@ data = dict(
])
],
samples_per_gpu=2 # Batch size of a single GPU used in testing
))
))
evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
interval=1, # Evaluation interval
metric=['bbox', 'segm']) # Metrics used during evaluation
@ -407,9 +407,15 @@ checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https:/
log_config = dict( # config to register logger hook
interval=50, # Interval to print the log
hooks=[
# dict(type='TensorboardLoggerHook') # The Tensorboard logger is also supported
dict(type='TextLoggerHook')
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False),
dict(type='MMDetWandbHook', by_epoch=False, # The Wandb logger is also supported, It requires `wandb` to be installed.
init_kwargs={'entity': "OpenMMLab", # The entity used to log on Wandb
'project': "MMDet", # Project name in WandB
'config': cfg_dict}), # Check https://docs.wandb.ai/ref/python/init for more init arguments.
# MMDetWandbHook is mmdet implementation of WandbLoggerHook. ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation.
]) # The logger used to record the training process.
dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set.
log_level = 'INFO' # The level of logging.
load_from = None # load models as a pre-trained model from a given path. This will not resume training.

155
docs/zh_cn/tutorials/config.md
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@ -56,8 +56,8 @@
```python
# 已经弃用的形式
model = dict(
type=...,
...
type=...,
...
)
train_cfg=dict(...)
test_cfg=dict(...)
@ -68,10 +68,10 @@ test_cfg=dict(...)
```python
# 推荐的形式
model = dict(
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
)
```
@ -93,7 +93,7 @@ model = dict(
requires_grad=True), # 是否训练归一化里的 gamma 和 beta。
norm_eval=True, # 是否冻结 BN 里的统计项。
style='pytorch', # 主干网络的风格，'pytorch' 意思是步长为2的层为 3x3 卷积， 'caffe' 意思是步长为2的层为 1x1 卷积。
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # 加载通过 ImageNet 预训练的模型
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # 加载通过 ImageNet 预训练的模型
neck=dict(
type='FPN', # 检测器的 neck 是 FPN，我们同样支持 'NASFPN', 'PAFPN' 等，更多细节可以参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10。
in_channels=[256, 512, 1024, 2048], # 输入通道数，这与主干网络的输出通道一致
@ -165,70 +165,70 @@ model = dict(
type='CrossEntropyLoss', # 用于分割的损失类型。
use_mask=True, # 是否只在正确的类中训练 mask。
loss_weight=1.0)))) # mask 分支的损失权重.
train_cfg = dict( # rpn 和 rcnn 训练超参数的配置
rpn=dict( # rpn 的训练配置
assigner=dict( # 分配器(assigner)的配置
type='MaxIoUAssigner', # 分配器的类型，MaxIoUAssigner 用于许多常见的检测器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
pos_iou_thr=0.7, # IoU >= 0.7(阈值) 被视为正样本。
neg_iou_thr=0.3, # IoU < 0.3(阈值) 被视为负样本。
min_pos_iou=0.3, # 将框作为正样本的最小 IoU 阈值。
match_low_quality=True, # 是否匹配低质量的框(更多细节见 API 文档).
ignore_iof_thr=-1), # 忽略 bbox 的 IoF 阈值。
sampler=dict( # 正/负采样器(sampler)的配置
type='RandomSampler', # 采样器类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
num=256, # 样本数量。
pos_fraction=0.5, # 正样本占总样本的比例。
neg_pos_ub=-1, # 基于正样本数量的负样本上限。
add_gt_as_proposals=False), # 采样后是否添加 GT 作为 proposal。
allowed_border=-1, # 填充有效锚点后允许的边框。
pos_weight=-1, # 训练期间正样本的权重。
debug=False), # 是否设置调试(debug)模式
rpn_proposal=dict( # 在训练期间生成 proposals 的配置
nms_across_levels=False, # 是否对跨层的 box 做 NMS。仅适用于 `GARPNHead` ，naive rpn 不支持 nms cross levels。
nms_pre=2000, # NMS 前的 box 数
nms_post=1000, # NMS 要保留的 box 的数量，只在 GARPNHHead 中起作用。
max_per_img=1000, # NMS 后要保留的 box 数量。
nms=dict( # NMS 的配置
type='nms', # NMS 的类别
iou_threshold=0.7 # NMS 的阈值
),
min_bbox_size=0), # 允许的最小 box 尺寸
rcnn=dict( # roi head 的配置。
assigner=dict( # 第二阶段分配器的配置，这与 rpn 中的不同
type='MaxIoUAssigner', # 分配器的类型，MaxIoUAssigner 目前用于所有 roi_heads。更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
pos_iou_thr=0.5, # IoU >= 0.5(阈值)被认为是正样本。
neg_iou_thr=0.5, # IoU < 0.5(阈值)被认为是负样本。
min_pos_iou=0.5, # 将 box 作为正样本的最小 IoU 阈值
match_low_quality=False, # 是否匹配低质量下的 box(有关更多详细信息，请参阅 API 文档)。
ignore_iof_thr=-1), # 忽略 bbox 的 IoF 阈值
sampler=dict(
type='RandomSampler', #采样器的类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
num=512, # 样本数量
pos_fraction=0.25, # 正样本占总样本的比例。.
neg_pos_ub=-1, # 基于正样本数量的负样本上限。.
add_gt_as_proposals=True
), # 采样后是否添加 GT 作为 proposal。
mask_size=28, # mask 的大小
pos_weight=-1, # 训练期间正样本的权重。
debug=False)) # 是否设置调试模式。
test_cfg = dict( # 用于测试 rpn 和 rcnn 超参数的配置
rpn=dict( # 测试阶段生成 proposals 的配置
nms_across_levels=False, # 是否对跨层的 box 做 NMS。仅适用于`GARPNHead`，naive rpn 不支持做 NMS cross levels。
nms_pre=1000, # NMS 前的 box 数
nms_post=1000, # NMS 要保留的 box 的数量，只在`GARPNHHead`中起作用。
max_per_img=1000, # NMS 后要保留的 box 数量
nms=dict( # NMS 的配置
type='nms', # NMS 的类型
iou_threshold=0.7 # NMS 阈值
),
min_bbox_size=0), # box 允许的最小尺寸
rcnn=dict( # roi heads 的配置
score_thr=0.05, # bbox 的分数阈值
nms=dict( # 第二步的 NMS 配置
type='nms', # NMS 的类型
iou_thr=0.5), # NMS 的阈值
max_per_img=100, # 每张图像的最大检测次数
mask_thr_binary=0.5)) # mask 预处的阈值
train_cfg = dict( # rpn 和 rcnn 训练超参数的配置
rpn=dict( # rpn 的训练配置
assigner=dict( # 分配器(assigner)的配置
type='MaxIoUAssigner', # 分配器的类型，MaxIoUAssigner 用于许多常见的检测器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
pos_iou_thr=0.7, # IoU >= 0.7(阈值) 被视为正样本。
neg_iou_thr=0.3, # IoU < 0.3(阈值) 被视为负样本。
min_pos_iou=0.3, # 将框作为正样本的最小 IoU 阈值。
match_low_quality=True, # 是否匹配低质量的框(更多细节见 API 文档).
ignore_iof_thr=-1), # 忽略 bbox 的 IoF 阈值。
sampler=dict( # 正/负采样器(sampler)的配置
type='RandomSampler', # 采样器类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
num=256, # 样本数量。
pos_fraction=0.5, # 正样本占总样本的比例。
neg_pos_ub=-1, # 基于正样本数量的负样本上限。
add_gt_as_proposals=False), # 采样后是否添加 GT 作为 proposal。
allowed_border=-1, # 填充有效锚点后允许的边框。
pos_weight=-1, # 训练期间正样本的权重。
debug=False), # 是否设置调试(debug)模式
rpn_proposal=dict( # 在训练期间生成 proposals 的配置
nms_across_levels=False, # 是否对跨层的 box 做 NMS。仅适用于 `GARPNHead` ，naive rpn 不支持 nms cross levels。
nms_pre=2000, # NMS 前的 box 数
nms_post=1000, # NMS 要保留的 box 的数量，只在 GARPNHHead 中起作用。
max_per_img=1000, # NMS 后要保留的 box 数量。
nms=dict( # NMS 的配置
type='nms', # NMS 的类别
iou_threshold=0.7 # NMS 的阈值
),
min_bbox_size=0), # 允许的最小 box 尺寸
rcnn=dict( # roi head 的配置。
assigner=dict( # 第二阶段分配器的配置，这与 rpn 中的不同
type='MaxIoUAssigner', # 分配器的类型，MaxIoUAssigner 目前用于所有 roi_heads。更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
pos_iou_thr=0.5, # IoU >= 0.5(阈值)被认为是正样本。
neg_iou_thr=0.5, # IoU < 0.5(阈值)被认为是负样本。
min_pos_iou=0.5, # 将 box 作为正样本的最小 IoU 阈值
match_low_quality=False, # 是否匹配低质量下的 box(有关更多详细信息，请参阅 API 文档)。
ignore_iof_thr=-1), # 忽略 bbox 的 IoF 阈值
sampler=dict(
type='RandomSampler', #采样器的类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
num=512, # 样本数量
pos_fraction=0.25, # 正样本占总样本的比例。.
neg_pos_ub=-1, # 基于正样本数量的负样本上限。.
add_gt_as_proposals=True
), # 采样后是否添加 GT 作为 proposal。
mask_size=28, # mask 的大小
pos_weight=-1, # 训练期间正样本的权重。
debug=False)) # 是否设置调试模式。
test_cfg = dict( # 用于测试 rpn 和 rcnn 超参数的配置
rpn=dict( # 测试阶段生成 proposals 的配置
nms_across_levels=False, # 是否对跨层的 box 做 NMS。仅适用于`GARPNHead`，naive rpn 不支持做 NMS cross levels。
nms_pre=1000, # NMS 前的 box 数
nms_post=1000, # NMS 要保留的 box 的数量，只在`GARPNHHead`中起作用。
max_per_img=1000, # NMS 后要保留的 box 数量
nms=dict( # NMS 的配置
type='nms', # NMS 的类型
iou_threshold=0.7 # NMS 阈值
),
min_bbox_size=0), # box 允许的最小尺寸
rcnn=dict( # roi heads 的配置
score_thr=0.05, # bbox 的分数阈值
nms=dict( # 第二步的 NMS 配置
type='nms', # NMS 的类型
iou_thr=0.5), # NMS 的阈值
max_per_img=100, # 每张图像的最大检测次数
mask_thr_binary=0.5)) # mask 预处的阈值
dataset_type = 'CocoDataset' # 数据集类型，这将被用来定义数据集。
data_root = 'data/coco/' # 数据的根路径。
img_norm_cfg = dict( # 图像归一化配置，用来归一化输入的图像。
@ -364,7 +364,7 @@ data = dict(
])
],
samples_per_gpu=2 # 单个 GPU 测试时的 Batch size
))
))
evaluation = dict( # evaluation hook 的配置，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7。
interval=1, # 验证的间隔。
metric=['bbox', 'segm']) # 验证期间使用的指标。
@ -389,10 +389,15 @@ checkpoint_config = dict( # Checkpoint hook 的配置文件。执行时请参
interval=1) # 保存的间隔是 1。
log_config = dict( # register logger hook 的配置文件。
interval=50, # 打印日志的间隔
hooks=[
# dict(type='TensorboardLoggerHook') # 同样支持 Tensorboard 日志
dict(type='TextLoggerHook')
hooks=[ # 训练期间执行的钩子
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False),
dict(type='MMDetWandbHook', by_epoch=False, # 还支持 Wandb 记录器，它需要安装 `wandb`。
init_kwargs={'entity': "OpenMMLab", # 用于登录wandb的实体
'project': "MMDet", # WandB中的项目名称
'config': cfg_dict}), # 检查 https://docs.wandb.ai/ref/python/init 以获取更多初始化参数
]) # 用于记录训练过程的记录器(logger)。
dist_params = dict(backend='nccl') # 用于设置分布式训练的参数，端口也同样可被设置。
log_level = 'INFO' # 日志的级别。
load_from = None # 从一个给定路径里加载模型作为预训练模型，它并不会消耗训练时间。

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